Overview

Dataset statistics

Number of variables13
Number of observations4269
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory433.7 KiB
Average record size in memory104.0 B

Variable types

Numeric10
Categorical2
Boolean1

Alerts

loan_id is uniformly distributedUniform
loan_id has unique valuesUnique
no_of_dependents has 712 (16.7%) zerosZeros
residential_assets_value has 45 (1.1%) zerosZeros
commercial_assets_value has 107 (2.5%) zerosZeros

Reproduction

Analysis started2024-07-14 16:45:56.493494
Analysis finished2024-07-14 16:46:04.447110
Duration7.95 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

loan_id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct4269
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2135
Minimum1
Maximum4269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-07-14T09:46:04.498031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile214.4
Q11068
median2135
Q33202
95-th percentile4055.6
Maximum4269
Range4268
Interquartile range (IQR)2134

Descriptive statistics

Standard deviation1232.4985
Coefficient of variation (CV)0.57728266
Kurtosis-1.2
Mean2135
Median Absolute Deviation (MAD)1067
Skewness0
Sum9114315
Variance1519052.5
MonotonicityStrictly increasing
2024-07-14T09:46:04.586063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2852 1
 
< 0.1%
2838 1
 
< 0.1%
2839 1
 
< 0.1%
2840 1
 
< 0.1%
2841 1
 
< 0.1%
2842 1
 
< 0.1%
2843 1
 
< 0.1%
2844 1
 
< 0.1%
2845 1
 
< 0.1%
Other values (4259) 4259
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
4269 1
< 0.1%
4268 1
< 0.1%
4267 1
< 0.1%
4266 1
< 0.1%
4265 1
< 0.1%
4264 1
< 0.1%
4263 1
< 0.1%
4262 1
< 0.1%
4261 1
< 0.1%
4260 1
< 0.1%

no_of_dependents
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4987116
Minimum0
Maximum5
Zeros712
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-07-14T09:46:04.657814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6959102
Coefficient of variation (CV)0.67871383
Kurtosis-1.2569922
Mean2.4987116
Median Absolute Deviation (MAD)1
Skewness-0.017970543
Sum10667
Variance2.8761113
MonotonicityNot monotonic
2024-07-14T09:46:04.745795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 752
17.6%
3 727
17.0%
0 712
16.7%
2 708
16.6%
1 697
16.3%
5 673
15.8%
ValueCountFrequency (%)
0 712
16.7%
1 697
16.3%
2 708
16.6%
3 727
17.0%
4 752
17.6%
5 673
15.8%
ValueCountFrequency (%)
5 673
15.8%
4 752
17.6%
3 727
17.0%
2 708
16.6%
1 697
16.3%
0 712
16.7%

education
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.5 KiB
Graduate
2144 
Not Graduate
2125 

Length

Max length12
Median length8
Mean length9.9910986
Min length8

Characters and Unicode

Total characters42652
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduate
2nd rowNot Graduate
3rd rowGraduate
4th rowGraduate
5th rowNot Graduate

Common Values

ValueCountFrequency (%)
Graduate 2144
50.2%
Not Graduate 2125
49.8%

Length

2024-07-14T09:46:04.825793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T09:46:04.897791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
graduate 4269
66.8%
not 2125
33.2%

Most occurring characters

ValueCountFrequency (%)
a 8538
20.0%
t 6394
15.0%
G 4269
10.0%
r 4269
10.0%
d 4269
10.0%
u 4269
10.0%
e 4269
10.0%
N 2125
 
5.0%
o 2125
 
5.0%
2125
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34133
80.0%
Uppercase Letter 6394
 
15.0%
Space Separator 2125
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8538
25.0%
t 6394
18.7%
r 4269
12.5%
d 4269
12.5%
u 4269
12.5%
e 4269
12.5%
o 2125
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
G 4269
66.8%
N 2125
33.2%
Space Separator
ValueCountFrequency (%)
2125
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40527
95.0%
Common 2125
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8538
21.1%
t 6394
15.8%
G 4269
10.5%
r 4269
10.5%
d 4269
10.5%
u 4269
10.5%
e 4269
10.5%
N 2125
 
5.2%
o 2125
 
5.2%
Common
ValueCountFrequency (%)
2125
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8538
20.0%
t 6394
15.0%
G 4269
10.0%
r 4269
10.0%
d 4269
10.0%
u 4269
10.0%
e 4269
10.0%
N 2125
 
5.0%
o 2125
 
5.0%
2125
 
5.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
True
2150 
False
2119 
ValueCountFrequency (%)
True 2150
50.4%
False 2119
49.6%
2024-07-14T09:46:04.969791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

income_annum
Real number (ℝ)

Distinct98
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5059123.9
Minimum200000
Maximum9900000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-07-14T09:46:05.049797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum200000
5-th percentile600000
Q12700000
median5100000
Q37500000
95-th percentile9400000
Maximum9900000
Range9700000
Interquartile range (IQR)4800000

Descriptive statistics

Standard deviation2806839.8
Coefficient of variation (CV)0.55480749
Kurtosis-1.182729
Mean5059123.9
Median Absolute Deviation (MAD)2400000
Skewness-0.012814425
Sum2.15974 × 1010
Variance7.8783498 × 1012
MonotonicityNot monotonic
2024-07-14T09:46:05.354250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7000000 62
 
1.5%
4100000 59
 
1.4%
7600000 57
 
1.3%
4700000 56
 
1.3%
6900000 55
 
1.3%
3200000 55
 
1.3%
5300000 55
 
1.3%
3900000 54
 
1.3%
8000000 53
 
1.2%
9000000 53
 
1.2%
Other values (88) 3710
86.9%
ValueCountFrequency (%)
200000 42
1.0%
300000 51
1.2%
400000 35
0.8%
500000 46
1.1%
600000 49
1.1%
700000 45
1.1%
800000 41
1.0%
900000 39
0.9%
1000000 42
1.0%
1100000 45
1.1%
ValueCountFrequency (%)
9900000 35
0.8%
9800000 48
1.1%
9700000 40
0.9%
9600000 39
0.9%
9500000 40
0.9%
9400000 47
1.1%
9300000 33
0.8%
9200000 49
1.1%
9100000 40
0.9%
9000000 53
1.2%

loan_amount
Real number (ℝ)

Distinct378
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15133450
Minimum300000
Maximum39500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-07-14T09:46:05.445033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile1800000
Q17700000
median14500000
Q321500000
95-th percentile30900000
Maximum39500000
Range39200000
Interquartile range (IQR)13800000

Descriptive statistics

Standard deviation9043363
Coefficient of variation (CV)0.59757443
Kurtosis-0.74367972
Mean15133450
Median Absolute Deviation (MAD)6900000
Skewness0.30872388
Sum6.46047 × 1010
Variance8.1782414 × 1013
MonotonicityNot monotonic
2024-07-14T09:46:05.600283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10600000 27
 
0.6%
20000000 24
 
0.6%
9400000 24
 
0.6%
16800000 23
 
0.5%
23900000 23
 
0.5%
11500000 22
 
0.5%
14100000 22
 
0.5%
12500000 22
 
0.5%
1800000 21
 
0.5%
3200000 21
 
0.5%
Other values (368) 4040
94.6%
ValueCountFrequency (%)
300000 6
 
0.1%
400000 7
 
0.2%
500000 16
0.4%
600000 13
0.3%
700000 15
0.4%
800000 15
0.4%
900000 12
0.3%
1000000 10
0.2%
1100000 18
0.4%
1200000 17
0.4%
ValueCountFrequency (%)
39500000 1
 
< 0.1%
38800000 1
 
< 0.1%
38700000 2
< 0.1%
38500000 1
 
< 0.1%
38400000 1
 
< 0.1%
38200000 3
0.1%
38000000 1
 
< 0.1%
37900000 2
< 0.1%
37800000 2
< 0.1%
37700000 1
 
< 0.1%

loan_term
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.900445
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-07-14T09:46:05.680356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q16
median10
Q316
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7091873
Coefficient of variation (CV)0.52375726
Kurtosis-1.2208527
Mean10.900445
Median Absolute Deviation (MAD)4
Skewness0.036358907
Sum46534
Variance32.594819
MonotonicityNot monotonic
2024-07-14T09:46:05.753385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 490
11.5%
12 456
10.7%
4 447
10.5%
10 436
10.2%
18 422
9.9%
16 412
9.7%
20 411
9.6%
14 405
9.5%
2 404
9.5%
8 386
9.0%
ValueCountFrequency (%)
2 404
9.5%
4 447
10.5%
6 490
11.5%
8 386
9.0%
10 436
10.2%
12 456
10.7%
14 405
9.5%
16 412
9.7%
18 422
9.9%
20 411
9.6%
ValueCountFrequency (%)
20 411
9.6%
18 422
9.9%
16 412
9.7%
14 405
9.5%
12 456
10.7%
10 436
10.2%
8 386
9.0%
6 490
11.5%
4 447
10.5%
2 404
9.5%

cibil_score
Real number (ℝ)

Distinct601
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean599.93605
Minimum300
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-07-14T09:46:05.841386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile330
Q1453
median600
Q3748
95-th percentile869
Maximum900
Range600
Interquartile range (IQR)295

Descriptive statistics

Standard deviation172.4304
Coefficient of variation (CV)0.28741463
Kurtosis-1.1856696
Mean599.93605
Median Absolute Deviation (MAD)147
Skewness-0.0090392773
Sum2561127
Variance29732.243
MonotonicityNot monotonic
2024-07-14T09:46:05.929386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
348 16
 
0.4%
543 15
 
0.4%
538 15
 
0.4%
778 14
 
0.3%
509 14
 
0.3%
439 14
 
0.3%
415 14
 
0.3%
819 14
 
0.3%
865 13
 
0.3%
802 13
 
0.3%
Other values (591) 4127
96.7%
ValueCountFrequency (%)
300 11
0.3%
301 8
0.2%
302 13
0.3%
303 6
0.1%
304 8
0.2%
305 3
 
0.1%
306 9
0.2%
307 8
0.2%
308 6
0.1%
309 6
0.1%
ValueCountFrequency (%)
900 6
0.1%
899 6
0.1%
898 5
0.1%
897 4
 
0.1%
896 11
0.3%
895 11
0.3%
894 5
0.1%
893 2
 
< 0.1%
892 8
0.2%
891 8
0.2%

residential_assets_value
Real number (ℝ)

ZEROS 

Distinct251
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7454860.6
Minimum-100000
Maximum24950000
Zeros45
Zeros (%)1.1%
Negative28
Negative (%)0.7%
Memory size33.5 KiB
2024-07-14T09:46:06.025386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-100000
5-th percentile300000
Q12200000
median5600000
Q311300000
95-th percentile21260000
Maximum24950000
Range25050000
Interquartile range (IQR)9100000

Descriptive statistics

Standard deviation6452441.6
Coefficient of variation (CV)0.86553484
Kurtosis0.028446239
Mean7454860.6
Median Absolute Deviation (MAD)4100000
Skewness0.94088012
Sum3.18248 × 1010
Variance4.1634002 × 1013
MonotonicityNot monotonic
2024-07-14T09:46:06.125515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400000 66
 
1.5%
500000 63
 
1.5%
100000 60
 
1.4%
1000000 57
 
1.3%
600000 56
 
1.3%
1300000 55
 
1.3%
700000 52
 
1.2%
300000 52
 
1.2%
24950000 52
 
1.2%
3200000 50
 
1.2%
Other values (241) 3706
86.8%
ValueCountFrequency (%)
-100000 28
0.7%
0 45
1.1%
100000 60
1.4%
200000 50
1.2%
300000 52
1.2%
400000 66
1.5%
500000 63
1.5%
600000 56
1.3%
700000 52
1.2%
800000 45
1.1%
ValueCountFrequency (%)
24950000 52
1.2%
24900000 2
 
< 0.1%
24800000 3
 
0.1%
24700000 2
 
< 0.1%
24600000 4
 
0.1%
24500000 3
 
0.1%
24400000 3
 
0.1%
24300000 3
 
0.1%
24200000 6
 
0.1%
24100000 4
 
0.1%

commercial_assets_value
Real number (ℝ)

ZEROS 

Distinct172
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4964289.1
Minimum0
Maximum17050000
Zeros107
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-07-14T09:46:06.207557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile200000
Q11300000
median3700000
Q37600000
95-th percentile13900000
Maximum17050000
Range17050000
Interquartile range (IQR)6300000

Descriptive statistics

Standard deviation4363080.5
Coefficient of variation (CV)0.87889331
Kurtosis-0.022425534
Mean4964289.1
Median Absolute Deviation (MAD)2800000
Skewness0.92870553
Sum2.119255 × 1010
Variance1.9036471 × 1013
MonotonicityNot monotonic
2024-07-14T09:46:06.299477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 107
 
2.5%
200000 101
 
2.4%
100000 100
 
2.3%
300000 90
 
2.1%
500000 83
 
1.9%
800000 76
 
1.8%
700000 74
 
1.7%
400000 71
 
1.7%
600000 70
 
1.6%
1000000 67
 
1.6%
Other values (162) 3430
80.3%
ValueCountFrequency (%)
0 107
2.5%
100000 100
2.3%
200000 101
2.4%
300000 90
2.1%
400000 71
1.7%
500000 83
1.9%
600000 70
1.6%
700000 74
1.7%
800000 76
1.8%
900000 52
1.2%
ValueCountFrequency (%)
17050000 37
0.9%
17000000 2
 
< 0.1%
16900000 3
 
0.1%
16800000 1
 
< 0.1%
16700000 4
 
0.1%
16600000 10
 
0.2%
16500000 8
 
0.2%
16400000 7
 
0.2%
16300000 4
 
0.1%
16200000 7
 
0.2%

luxury_assets_value
Real number (ℝ)

Distinct379
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15126306
Minimum300000
Maximum39200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-07-14T09:46:06.395478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile1900000
Q17500000
median14600000
Q321700000
95-th percentile31300000
Maximum39200000
Range38900000
Interquartile range (IQR)14200000

Descriptive statistics

Standard deviation9103753.7
Coefficient of variation (CV)0.6018491
Kurtosis-0.73805613
Mean15126306
Median Absolute Deviation (MAD)7100000
Skewness0.3222075
Sum6.45742 × 1010
Variance8.2878331 × 1013
MonotonicityNot monotonic
2024-07-14T09:46:06.491699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6200000 26
 
0.6%
2900000 26
 
0.6%
20400000 26
 
0.6%
12300000 24
 
0.6%
12000000 24
 
0.6%
14900000 24
 
0.6%
7100000 23
 
0.5%
17200000 23
 
0.5%
14100000 22
 
0.5%
9900000 22
 
0.5%
Other values (369) 4029
94.4%
ValueCountFrequency (%)
300000 4
 
0.1%
400000 10
0.2%
500000 14
0.3%
600000 14
0.3%
700000 14
0.3%
800000 16
0.4%
900000 13
0.3%
1000000 8
0.2%
1100000 19
0.4%
1200000 16
0.4%
ValueCountFrequency (%)
39200000 1
 
< 0.1%
39100000 1
 
< 0.1%
38600000 2
 
< 0.1%
38200000 4
0.1%
38100000 5
0.1%
38000000 1
 
< 0.1%
37900000 2
 
< 0.1%
37800000 2
 
< 0.1%
37700000 1
 
< 0.1%
37600000 2
 
< 0.1%

bank_asset_value
Real number (ℝ)

Distinct143
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4976341.1
Minimum0
Maximum14300000
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2024-07-14T09:46:06.587641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile500000
Q12300000
median4600000
Q37100000
95-th percentile11100000
Maximum14300000
Range14300000
Interquartile range (IQR)4800000

Descriptive statistics

Standard deviation3249158.6
Coefficient of variation (CV)0.6529212
Kurtosis-0.40455279
Mean4976341.1
Median Absolute Deviation (MAD)2400000
Skewness0.55880946
Sum2.1244 × 1010
Variance1.0557032 × 1013
MonotonicityNot monotonic
2024-07-14T09:46:06.683692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3600000 63
 
1.5%
4900000 63
 
1.5%
1400000 63
 
1.5%
4500000 61
 
1.4%
1600000 60
 
1.4%
5400000 59
 
1.4%
900000 58
 
1.4%
1300000 57
 
1.3%
4100000 54
 
1.3%
200000 54
 
1.3%
Other values (133) 3677
86.1%
ValueCountFrequency (%)
0 8
 
0.2%
100000 31
0.7%
200000 54
1.3%
300000 50
1.2%
400000 50
1.2%
500000 40
0.9%
600000 50
1.2%
700000 42
1.0%
800000 39
0.9%
900000 58
1.4%
ValueCountFrequency (%)
14300000 6
0.1%
14200000 2
 
< 0.1%
14100000 3
0.1%
14000000 3
0.1%
13900000 4
0.1%
13800000 2
 
< 0.1%
13700000 1
 
< 0.1%
13600000 3
0.1%
13500000 4
0.1%
13400000 4
0.1%

loan_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.5 KiB
Approved
2656 
Rejected
1613 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters34152
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApproved
2nd rowRejected
3rd rowRejected
4th rowRejected
5th rowRejected

Common Values

ValueCountFrequency (%)
Approved 2656
62.2%
Rejected 1613
37.8%

Length

2024-07-14T09:46:06.771688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T09:46:06.835693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
approved 2656
62.2%
rejected 1613
37.8%

Most occurring characters

ValueCountFrequency (%)
e 7495
21.9%
p 5312
15.6%
d 4269
12.5%
A 2656
 
7.8%
r 2656
 
7.8%
o 2656
 
7.8%
v 2656
 
7.8%
R 1613
 
4.7%
j 1613
 
4.7%
c 1613
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29883
87.5%
Uppercase Letter 4269
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7495
25.1%
p 5312
17.8%
d 4269
14.3%
r 2656
 
8.9%
o 2656
 
8.9%
v 2656
 
8.9%
j 1613
 
5.4%
c 1613
 
5.4%
t 1613
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A 2656
62.2%
R 1613
37.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 34152
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7495
21.9%
p 5312
15.6%
d 4269
12.5%
A 2656
 
7.8%
r 2656
 
7.8%
o 2656
 
7.8%
v 2656
 
7.8%
R 1613
 
4.7%
j 1613
 
4.7%
c 1613
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7495
21.9%
p 5312
15.6%
d 4269
12.5%
A 2656
 
7.8%
r 2656
 
7.8%
o 2656
 
7.8%
v 2656
 
7.8%
R 1613
 
4.7%
j 1613
 
4.7%
c 1613
 
4.7%

Interactions

2024-07-14T09:46:03.526079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:56.834155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.466249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:58.121405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.172815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.854458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:00.526637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:01.254902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:02.126010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:02.838093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:03.590079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:56.890155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.530249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:58.177418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.230161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.918536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:00.586721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:01.312515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:02.190009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:02.902077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:03.654079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:56.946096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.586249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:58.545857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.302172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.982459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:00.682722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:01.376481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:02.278003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:02.966087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:03.718080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.010156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.650249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:58.608834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.366173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:00.046458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:00.748580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:01.440944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:02.341977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:03.030078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:03.790120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.074145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.722313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:58.675346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.438172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:00.110494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:00.828764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:01.512951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:02.414042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:03.102080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:03.862080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.138166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.786303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:58.747248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.502172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:00.182459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:00.901385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:01.785118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:02.494033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:03.166083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:03.934516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.202156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.850341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:58.884404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.574173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:00.246460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:00.965386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:01.849129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:02.558091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:03.238078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:03.998516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.266145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.914376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:58.964499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.638173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:00.310459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:01.029386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:01.913130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:02.630077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:03.310155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:04.070516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.330121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.981081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.029359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.710173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:00.390459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:01.101386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:01.980760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:02.694084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:03.382079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:04.142517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:57.402256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:58.053080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.108177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:45:59.782173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:00.462545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:01.181401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:02.052876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:02.766077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-14T09:46:03.454154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-07-14T09:46:04.238531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-14T09:46:04.383042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

loan_idno_of_dependentseducationself_employedincome_annumloan_amountloan_termcibil_scoreresidential_assets_valuecommercial_assets_valueluxury_assets_valuebank_asset_valueloan_status
012GraduateNo96000002990000012778240000017050000227000008000000Approved
120Not GraduateYes41000001220000084172700000220000088000003300000Rejected
233GraduateNo91000002970000020506710000045000003330000012800000Rejected
343GraduateNo8200000307000008467182000003300000233000007900000Rejected
455Not GraduateYes98000002420000020382124000008200000294000005000000Rejected
560GraduateYes4800000135000001031968000008300000137000005100000Rejected
675GraduateNo87000003300000046782250000014800000292000004300000Approved
782GraduateYes57000001500000020382132000005700000118000006000000Rejected
890GraduateYes80000022000002078213000008000002800000600000Approved
9105Not GraduateNo11000004300000103883200000140000033000001600000Rejected
loan_idno_of_dependentseducationself_employedincome_annumloan_amountloan_termcibil_scoreresidential_assets_valuecommercial_assets_valueluxury_assets_valuebank_asset_valueloan_status
425942600Not GraduateYes45000001150000014509134000002300000154000005900000Rejected
426042615GraduateNo880000029300000105601680000013900000311000009900000Approved
426142623GraduateYes3000000750000068811400000450000061000002300000Approved
426242635GraduateNo13000003000000205401000000230000032000001900000Rejected
426342643GraduateNo5000000127000001486547000008100000195000006300000Approved
426442655GraduateYes100000023000001231728000005000003300000800000Rejected
426542660Not GraduateYes3300000113000002055942000002900000110000001900000Approved
426642672Not GraduateNo65000002390000018457120000012400000181000007300000Rejected
426742681Not GraduateNo41000001280000087808200000700000141000005800000Approved
426842691GraduateNo9200000297000001060717800000118000003570000012000000Approved